2.4 Pulse 3
65
Through the AIMS technique, researchers and practitioners can get a more
nuanced and deeper understanding of issues and their causal substance. This is
because ‘[a] causal mechanism provides an explanatory account of observed results
by describing the mediating process by which the target factor could have produced
the effect’ (Koslowski et al. 1989: 1317). In this respect, knowledge aims to understand phenomena and issues, while causal mechanisms provide an understanding of
the ‘What if?’ questions (Hedström and Ylikoski 2010).
Causal mechanisms are important for understanding causation (p. 55), and vice
versa (Falleti and Lynch 2009), in any field of study and at any level. Elster (1998:
45, cited in Guzzini 2011; Elster 2007: 36) conceptualised causal mechanisms as
‘frequently occurring and easily recognisable causal patterns that are triggered under
generally unknown conditions or with indeterminate conditions. They allow us to
explain, but not to predict’. Guzzini (2011) argued that not all causal mechanisms
are observable and observed. We can assign observable mechanisms to true causal
statements (Elster 2007), but causal mechanisms are often ‘hidden’ (Guzzini 2011).
Elster continued, by asserting that:
To cite a cause is not enough: the causal mechanism must also be provided, or at least
suggested. In everyday language, in good novels, in good historical writings, and in many
social scientific analyses, the mechanism is not explicitly cited. Instead, it is suggested by
the way in which the cause is described.
As Goertz and Mahoney (2010) observed, when people see data relating to the
association between two variables, they often request additional information on the
mechanisms, before concluding that the association is causal in nature.
Causal mechanisms describe the relationships or actions among the units of analysis, or the cases under investigation, within a study (Falleti and Lynch 2009, cited in
Guzzini 2011). Causal mechanisms show why something has happened (Guzzini
2011) by providing ‘an explanatory account of observed results by describing
the mediating process by which the target factor could have produced the effect’
(Koslowski et al. 1989: 1317). Should researchers reduce causal mechanisms to
variables, they would be denying the ‘possibility of a combinational or configurational explanation’, with the interpretation being part of the methodology of causal
mechanisms (Guzzini 2011: 333). For Falleti and Lynch (2009: 1143), causal mechanisms are transferable concepts that explain how and why a contextualised cause
primes certain outcomes. Their study ‘defines context as the relevant aspect of a
setting in which an array of initial conditions leads to an outcome of a defined scope
and meaning, via causal mechanisms’ (Falleti and Lynch 2009: 1143).
Researchers therefore need to be attentive to the interplay between context and
causal mechanisms, irrespective of the method that they employ, be it small-sample,
formal, statistical or interpretive (Falleti and Lynch 2009). Hedström and Ylikoski
(2010) affirmed this when they said that causal mechanisms are the processes that
are characterised by the relations and interactions of a system’s parts, its structure
and environment, or context. In his definition of a causal mechanism, Rueschemeyer
(2009: 21) noted that it is ‘…a condition, a relation, or a process that brings about
certain events and states’. These occasions and conditions play out in specific
65
Through the AIMS technique, researchers and practitioners can get a more
nuanced and deeper understanding of issues and their causal substance. This is
because ‘[a] causal mechanism provides an explanatory account of observed results
by describing the mediating process by which the target factor could have produced
the effect’ (Koslowski et al. 1989: 1317). In this respect, knowledge aims to understand phenomena and issues, while causal mechanisms provide an understanding of
the ‘What if?’ questions (Hedström and Ylikoski 2010).
Causal mechanisms are important for understanding causation (p. 55), and vice
versa (Falleti and Lynch 2009), in any field of study and at any level. Elster (1998:
45, cited in Guzzini 2011; Elster 2007: 36) conceptualised causal mechanisms as
‘frequently occurring and easily recognisable causal patterns that are triggered under
generally unknown conditions or with indeterminate conditions. They allow us to
explain, but not to predict’. Guzzini (2011) argued that not all causal mechanisms
are observable and observed. We can assign observable mechanisms to true causal
statements (Elster 2007), but causal mechanisms are often ‘hidden’ (Guzzini 2011).
Elster continued, by asserting that:
To cite a cause is not enough: the causal mechanism must also be provided, or at least
suggested. In everyday language, in good novels, in good historical writings, and in many
social scientific analyses, the mechanism is not explicitly cited. Instead, it is suggested by
the way in which the cause is described.
As Goertz and Mahoney (2010) observed, when people see data relating to the
association between two variables, they often request additional information on the
mechanisms, before concluding that the association is causal in nature.
Causal mechanisms describe the relationships or actions among the units of analysis, or the cases under investigation, within a study (Falleti and Lynch 2009, cited in
Guzzini 2011). Causal mechanisms show why something has happened (Guzzini
2011) by providing ‘an explanatory account of observed results by describing
the mediating process by which the target factor could have produced the effect’
(Koslowski et al. 1989: 1317). Should researchers reduce causal mechanisms to
variables, they would be denying the ‘possibility of a combinational or configurational explanation’, with the interpretation being part of the methodology of causal
mechanisms (Guzzini 2011: 333). For Falleti and Lynch (2009: 1143), causal mechanisms are transferable concepts that explain how and why a contextualised cause
primes certain outcomes. Their study ‘defines context as the relevant aspect of a
setting in which an array of initial conditions leads to an outcome of a defined scope
and meaning, via causal mechanisms’ (Falleti and Lynch 2009: 1143).
Researchers therefore need to be attentive to the interplay between context and
causal mechanisms, irrespective of the method that they employ, be it small-sample,
formal, statistical or interpretive (Falleti and Lynch 2009). Hedström and Ylikoski
(2010) affirmed this when they said that causal mechanisms are the processes that
are characterised by the relations and interactions of a system’s parts, its structure
and environment, or context. In his definition of a causal mechanism, Rueschemeyer
(2009: 21) noted that it is ‘…a condition, a relation, or a process that brings about
certain events and states’. These occasions and conditions play out in specific